Research Summaries

Back Bi-Dimensional Empirical Mode Decomposition (BEMD) for Mine Detection and Change Detection

Fiscal Year 2010
Division Research & Sponsored Programs
Department Meyer Institute
Investigator(s) Chu, Peter C.
Sponsor Naval Oceanographic Office (Navy)
Summary Automatic detection of sea mines in coastal regions is a difficult task due to the highly variable sea bottom conditions present in the underwater environment. Detection systems must be able to discriminate objects which vary in size, shape, and orientation from naturally occurring and man-made clutter. Additionally, these automated systems must be computationally efficient to be incorporated into Unmanned Underwater Vehicle (UUV) sensor systems characterized by high sensor data rates and limited processing abilities. Using empirical mode decomposition analysis, a fast, robust sea mine detection and change detection systems can be created. These decompositions project key image features, geometrically defined structures with orientations, and localized information into distinct orthogonal components or feature subspaces of the image. The performance of the new detection system is compared against the performance of an independent detection system in terms of probability of detection (Pd) and probability of false alarm (Pfa).
Keywords Mine Warfare Sediment Types Littoral Oceanography Survey Periodicity Analogous Areas
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Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal